scholarly journals Seismic wave simulation of a complex foothill belt

2020 ◽  
Vol 17 (5) ◽  
pp. 893-905
Author(s):  
Weihua Zhang ◽  
Li Yang ◽  
Wenpeng Si ◽  
Houyu Liu
Keyword(s):  
Surface Waves ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Single Shot ◽  
Forward Modelling ◽  
Underground Structures ◽  
Near Surface ◽  
Modelling Method ◽  
Propagation Law ◽  
Strong Surface

Abstract Foothill belts ‘dual-complexity’ of the surface and underground structures hinders an accurate seismic imaging of complex geological structures. In this paper, the propagation law of the seismic wavefield in the foothill belt is studied through seismic forward modelling and its influences on the seismic data acquisition and imaging. A foothill belt with typical ‘dual-complexity’ characteristics is investigated. Single-shot records and their imaging effects simulated with different absorption coefficients and different near-surface structure models are analysed. The results suggest that strong surface waves and their scattered noise generated by the complex near surface in the foothill belt are the main reasons for the low signal-to-noise ratio and difficulties in the imaging process of seismic data. The viscoelastic-medium modelling method effectively suppresses the surface waves and their scattered noise, which improves the seismic data quality and imaging in the foothill belt, and thus is a suitable forward modelling method for the foothill belts.

SEISMIC DATA ENHANCEMENT—A CASE HISTORY

Geophysics ◽  
1960 ◽  
Vol 25 (1) ◽  
pp. 283-311 ◽  
Author(s):  
R. J. Graebner
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Wave Length ◽  
Frequency Range ◽  
Near Surface ◽  
Theory To Practice ◽  
Special Character ◽  
Strong Surface ◽  
Data Processing Methods

The theory relating to many methods—for example, multiple seismometer techniques—which the geophysicist may control to improve record quality is well known. However, its application has not been fully exploited. An example of the reduction of theory to practice in one area characterized by poor records is presented. It comprises a series of analytical tests designed to discover the cause of poor records, to examine the effect of each variable on the signal‐to‐noise ratio, and to evaluate the solutions predicted by theory. The tests showed that the poor record quality was attributable chiefly to relatively strong surface and near‐surface waves propagating outward from the shot. Wave length filtering by means of suitable shot and seismometer patterns, and compositing through data processing methods, greatly improved record quality and permitted magnetic recording of reflected signals over a broad frequency range. The tests established, in the allotted time, that the quality of the data would meet clearly specified standards of performance. Experience has shown that better seismic data can generally be obtained when the design of techniques is based on the special character of the signal and noise determined from simple tests rather than when the design is based on general assumptions.

Slant f-k transform of multichannel seismic surface wave data

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. A19-A24 ◽  
Author(s):  
Aleksander S. Serdyukov ◽  
Aleksander V. Yablokov ◽  
Anton A. Duchkov ◽  
Anton A. Azarov ◽  
Valery D. Baranov
Keyword(s):  
Surface Wave ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Velocity Estimation ◽  
Seismic Exploration ◽  
Spectral Processing ◽  
Near Surface ◽  
Frequency Time Representation ◽  
Time Representation ◽  
S Transform

We have addressed the problem of estimating surface-wave phase velocities through the spectral processing of seismic data. This is the key step of the well-known near-surface seismic exploration method, called multichannel analysis of surface waves. To increase the accuracy and ensure the unambiguity of the selection of dispersion curves, we have developed a new version of the frequency-wavenumber ([Formula: see text]-[Formula: see text]) transform based on the S-transform. We obtain the frequency-time representation of seismic data. We analyze the obtained S-transform frequency-time representation in a slant-stacking manner but use a spatial Fourier transform instead of amplitude stacking. Finally, we build the [Formula: see text]-[Formula: see text] image by analyzing the spatial spectra for different steering values of the surface-wave group velocities. The time localization of the surface-wave packet at each frequency increases the signal-to-noise ratio because of an exclusion of noise in other time steps (which does not fall in the effective width of the corresponding wavelet). The new [Formula: see text]-[Formula: see text] transform, i.e., the slant [Formula: see text]-[Formula: see text] (SFK) transform, renders a better spectral analysis than the conventional [Formula: see text]-[Formula: see text] transform and yields more accurate phase-velocity estimation, which is critical for the surface-wave analysis. The advantages of the SFK transform have been confirmed by synthetic- and field-data processing.

Estimating randomness using seismic disorder

2014 ◽  
Vol 2 (1) ◽  
pp. SA93-SA97 ◽  
Author(s):  
Saleh Al-Dossary ◽  
Yuchun Eugene Wang ◽  
Mark McFarlane
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Hydrocarbon Exploration ◽  
Estimation Methods ◽  
Fracture Zones ◽  
Near Surface ◽  
Filter Operation ◽  
Gas Chimneys ◽  
Engineering Projects ◽  
Assess Data Quality

The new seismic disorder attribute quantitatively describes the degree of randomness embedded in 3D poststack seismic data. We compute seismic disorder using a filter operation that removes simple structures including constant values, constant slopes, and steps in axial directions. We define the power of the filtered data as the seismic disorder attribute, which approximately represents data randomness. Seismic data irregularities are caused by a variety of reasons, including random reflection, diffraction, near-surface variations, and acquisition noise. Consequently, the spatial distribution of the seismic disorder attribute may help hydrocarbon exploration in several ways, including identifying geologic features such as fracture zones, gas chimneys, and terminated unconformities; indicating the signal-to-noise ratio to assess data quality; and providing a confidence index for reservoir simulation and engineering projects. We present three case studies and a comparison to other noise-estimation methods.

Triangle Ranch Headquarters field development using shallow core holes and high‐resolution seismic data

Geophysics ◽  
1989 ◽  
Vol 54 (11) ◽  
pp. 1384-1396
Author(s):  
Howard Renick ◽  
R. D. Gunn
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Processing Parameters ◽  
Mapping Method ◽  
High Signal ◽  
Signal To Noise ◽  
Near Surface ◽  
Field Development ◽  
Reef Structure ◽  
Noise Ratio

The Triangle Ranch Headquarters Canyon Reef field is long and narrow and in an area where near‐surface evaporites and associated collapse features degrade seismic data quality and interpretational reliability. Below this disturbed section, the structure of rocks is similar to the deeper Canyon Reef structure. The shallow structure exhibits very gentle relief and can be mapped by drilling shallow holes on a broad grid. The shallow structural interpretation provides a valuable reference datum for mapping, as well as providing a basis for planning a seismic program. By computing an isopach between the variable seismic datum and the Canyon Reef reflection and subtracting the isopach map from the datum map, we map Canyon Reef structure. The datum map is extrapolated from the shallow core holes. In the area, near‐surface complexities produce seismic noise and severe static variations. The crux of the exploration problem is to balance seismic signal‐to‐noise ratio and geologic resolution. Adequate geologic resolution is impossible without understanding the exploration target. As we understood the target better, we modified our seismic acquisition parameters. Studying examples of data with high signal‐to‐noise ratio and poor resolution and examples of better defined structure on apparently noisier data led us to design an acquisition program for resolution and to reduce noise with arithmetic processes that do not reduce structural resolution. Combining acquisition and processing parameters for optimum structural resolution with the isopach mapping method has improved wildcat success from about 1 in 20 to better than 1 in 2. It has also enabled an 80 percent development drilling success ratio as opposed to slightly over 50 percent in all previous drilling.

scholarly journals Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S197-S205 ◽  
Author(s):  
Zhaolun Liu ◽  
Abdullah AlTheyab ◽  
Sherif M. Hanafy ◽  
Gerard Schuster
Keyword(s):  
Surface Waves ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
3D Data ◽  
Strong Surface ◽  
Migration Method ◽  
Recorded Data

We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength [Formula: see text] of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third [Formula: see text]. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half [Formula: see text].

Stacking of surface waves

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V51-V58 ◽  
Author(s):  
Boriszláv Neducza
Keyword(s):  
Data Quality ◽  
Surface Waves ◽  
Signal To Noise Ratio ◽  
Dispersion Curves ◽  
Horizontal Resolution ◽  
Coherent Noise ◽  
Data Set ◽  
Near Surface ◽  
Advantages And Disadvantages

The seismic surface wave method (SWM) is a powerful means of characterizing near-surface structures. Although the SWM consists of only three steps (data acquisition, determination of dispersion curves, and inversion), it is important to take considerable care with the second step, determination of the dispersion curves. This step is usually completed by spectral analysis of surface waves (SASW) or multichannel analysis of surface waves (MASW). However, neither method is ideal, as each has its advantages and disadvantages. SASW provides higher horizontal resolution, but it is very sensitive to coherent noise and individual geophone coupling. MASW is a robust method able to separate different wave types, but its horizontal resolution is lower. Stacking of surface waves (SSW) is a good compromise between SASW and MASW. Using a reduced number of traces increases the horizontal resolution of MASW, and utilizing other shot records with the same receivers compensates for the decreased signal-to-noise ratio. The stacking is realized by summing the [Formula: see text] amplitude spectra of windowed shot records, where windowing produces higher horizontal resolution and stacking produces improved data quality. Mixing is applied between the stacks derived with different parameters, as different frequency ranges require different windowing. SSW was tested and corroborated on a deep seismic data set. Horizontal resolution is validated by [Formula: see text] plots at different frequencies, and [Formula: see text] plots present data quality.

Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network

2020 ◽  
Vol 221 (2) ◽  
pp. 1211-1225 ◽  
Author(s):  
Y X Zhao ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Negative Impact ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Seismic Data Processing ◽  
Underground Structures ◽  
Seismic Records

SUMMARY One of the difficulties in desert seismic data processing is the large spectral overlap between noise and reflected signals. Existing denoising algorithms usually have a negative impact on the resolution and fidelity of seismic data when denoising, which is not conducive to the acquisition of underground structures and lithology related information. Aiming at this problem, we combine traditional method with deep learning, and propose a new feature extraction and denoising strategy based on a convolutional neural network, namely VMDCNN. In addition, we also build a training set using field seismic data and synthetic seismic data to optimize network parameters. The processing results of synthetic seismic records and field seismic records show that the proposed method can effectively suppress the noise that shares the same frequency band with the reflected signals, and the reflected signals have almost no energy loss. The processing results meet the requirements of high signal-to-noise ratio, high resolution and high fidelity for seismic data processing.

scholarly journals Interferometric ground-roll removal: Attenuation of scattered surface waves in single-sensor data

Geophysics ◽  
2010 ◽  
Vol 75 (2) ◽  
pp. SA15-SA25 ◽  
Author(s):  
David F. Halliday ◽  
Andrew Curtis ◽  
Peter Vermeer ◽  
Claudio Strobbia ◽  
Anna Glushchenko ◽  
...  
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Seismic Data ◽  
Sensor Data ◽  
Seismic Survey ◽  
Seismic Interferometry ◽  
Near Surface ◽  
Reflection Data ◽  
Ground Roll ◽  
Single Sensor

Land seismic data are contaminated by surface waves (or ground roll). These surface waves are a form of source-generated noise and can be strongly scattered by near-surface heterogeneities. The resulting scattered ground roll can be particularly difficult to separate from the desired reflection data, especially when this scattered ground roll propagates in the crossline direction. We have used seismic interferometry to estimate scattered surface waves, recorded during an exploration seismic survey, between pairs of receiver locations. Where sources and receivers coincide, these interreceiver surface-wave estimates were adaptively subtracted from the data. This predictive-subtraction process can successfully attenuate scattered surface waves while preserving the valuable reflected arrivals, forming a new method of scattered ground-roll attenuation. We refer to this as interferometric ground-roll removal.

Nonlinear beamforming for enhancement of 3D prestack land seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V283-V296 ◽  
Author(s):  
Andrey Bakulin ◽  
Ilya Silvestrov ◽  
Maxim Dmitriev ◽  
Dmitry Neklyudov ◽  
Maxim Protasov ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Sensor Data ◽  
Reservoir Properties ◽  
Near Surface ◽  
3D Data ◽  
Common Reflection Surface ◽  
Single Sensor ◽  
The Common

We have developed nonlinear beamforming (NLBF), a method for enhancing modern 3D prestack seismic data acquired onshore with small field arrays or single sensors in which weak reflected signals are buried beneath the strong scattered noise induced by a complex near surface. The method is based on the ideas of multidimensional stacking techniques, such as the common-reflection-surface stack and multifocusing, but it is designed specifically to improve the prestack signal-to-noise ratio of modern 3D land seismic data. Essentially, NLBF searches for coherent local events in the prestack data and then performs beamforming along the estimated surfaces. Comparing different gathers that can be extracted from modern 3D data acquired with orthogonal acquisition geometries, we determine that the cross-spread domain (CSD) is typically the most convenient and efficient. Conventional noise removal applied to modern data from small arrays or single sensors does not adequately reveal the underlying reflection signal. Instead, NLBF supplements these conventional tools and performs final aggregation of weak and still broken reflection signals, where the strength is controlled by the summation aperture. We have developed the details of the NLBF algorithm in CSD and determined the capabilities of the method on real 3D land data with the focus on enhancing reflections and early arrivals. We expect NLBF to help streamline seismic processing of modern high-channel-count and single-sensor data, leading to improved images as well as better prestack data for estimation of reservoir properties.

Analysis and Application on Frequency-Response Charateristic Based on Multi-Elements in Near-Surface

2014 ◽  
Vol 490-491 ◽  
pp. 1548-1552
Author(s):  
Zhi Xin Di
Keyword(s):  
Surface Layer ◽  
Seismic Data ◽  
Yellow River ◽  
Source Area ◽  
Yellow River Delta ◽  
Single Shot ◽  
Near Surface ◽  
Explosive Source ◽  
Layer Velocity

With the continuously process of prospecting program, our land exploration enter into activity lithostratigrapgy stage. For searching medium and small or subtle reservior, higher seismic data discernibility must be needed. In the explosive source area, surface layer velocity, shot lithology and ghost interface are the three key elements influencing the shot frequency. In view of the trait that the quality of single shot has apparently difference causing by near surface layer Yellow River Delta multiple lithology alternating deposits, we study the characteristics of frequency reponse to lithology and ghost by microseismogram log data, to provide reliable basis for scientific select shooting parameter.

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